A number of studies have proposed intricate methods to mitigate the detrimental effects of heterogeneity. Specifically, we have identified methods such as FedProx [1]} and FSVRG [2]} which apply local model/gradient controlled learning that limits the divergence of clients. Another known method, as seen in FeSEM [3]} and MLMG [4]} is to group clients or create more than one server model based on clients similarity.
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